Search Results for author:
Found 32 papers, 11 papers with code
Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs
To better comprehend the complex physical laws, this paper proposes a novel learning based simulation model- Graph Networks with Spatial-Temporal neural Ordinary Equations (GNSTODE)- that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework.
How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting?
In this paper, we establish a theoretical framework that unravels the expressive power of spectral-temporal GNNs.
LAVA: Data Valuation without Pre-Specified Learning Algorithms
(1) We develop a proxy for the validation performance associated with a training set based on a non-conventional class-wise Wasserstein distance between the training and the validation set.
Geometric Relational Embeddings: A Survey
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions.
Hierarchical Multi-label Classification
Knowledge Graph Completion
+1
Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance
Modern power systems will have to face difficult challenges in the years to come: frequent blackouts in urban areas caused by high power demand peaks, grid instability exacerbated by intermittent renewable generation, and global climate change amplified by rising carbon emissions.
On Solution Functions of Optimization: Universal Approximation and Covering Number Bounds
We study the expressibility and learnability of convex optimization solution functions and their multi-layer architectural extension.
Non-stationary Risk-sensitive Reinforcement Learning: Near-optimal Dynamic Regret, Adaptive Detection, and Separation Design
We study risk-sensitive reinforcement learning (RL) based on an entropic risk measure in episodic non-stationary Markov decision processes (MDPs).
Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning
Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can significantly impact performance.
Recognizing Nested Entities from Flat Supervision: A New NER Subtask, Feasibility and Challenges
However, existing nested NER models heavily rely on training data annotated with nested entities, while labeling such data is costly.
Variational Bayesian Inference Clustering Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC
Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference based clustering (VBIC) algorithm is developed to solve this clustering problem.
How to Sift Out a Clean Data Subset in the Presence of Data Poisoning?
Most poisoning defenses presume access to a set of clean data (or base set).
Learning Neural Networks under Input-Output Specifications
In this paper, we examine an important problem of learning neural networks that certifiably meet certain specifications on input-output behaviors.
Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs
Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach
Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce.
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
To overcome the aforementioned problems, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme.
Adversarial Unlearning of Backdoors via Implicit Hypergradient
Particularly, its performance is more robust to the variation on triggers, attack settings, poison ratio, and clean data size.
Towards General Robustness to Bad Training Data
In this paper, we focus on the problem of identifying bad training data when the underlying cause is unknown in advance.
Spatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs
Spatiotemporal representation learning on multivariate time series has received tremendous attention in forecasting traffic and energy data.
Recurrent Neural Network Controllers Synthesis with Stability Guarantees for Partially Observed Systems
Neural network controllers have become popular in control tasks thanks to their flexibility and expressivity.
Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection
While the generative attribute regression module allows us to capture the anomalies in the attribute space, the multi-view contrastive learning module can exploit richer structure information from multiple subgraphs, thus abling to capture the anomalies in the structure space, mixing of structure, and attribute information.
MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition
Although several studies have adopted domain adaptation (DA) approaches to tackle this problem, most of them treat multiple EEG data from different subjects and sessions together as a single source domain for transfer, which either fails to satisfy the assumption of domain adaptation that the source has a certain marginal distribution, or increases the difficulty of adaptation.
A Unified Framework for Task-Driven Data Quality Management
High-quality data is critical to train performant Machine Learning (ML) models, highlighting the importance of Data Quality Management (DQM).
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning.
Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach
The trained DNNs can be driven by partial, noisy, or proxy descriptors of the current grid conditions.
Graph Self-Supervised Learning: A Survey
Deep learning on graphs has attracted significant interests recently.
Unitary Approximate Message Passing for Sparse Bayesian Learning
Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm.
Imitation Learning with Stability and Safety Guarantees
A method is presented to learn neural network (NN) controllers with stability and safety guarantees through imitation learning (IL).
Power up! Robust Graph Convolutional Network based on Graph Powering
By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.
Power up! Robust Graph Convolutional Network via Graph Powering
By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.
Stability-certified reinforcement learning: A control-theoretic perspective
We investigate the important problem of certifying stability of reinforcement learning policies when interconnected with nonlinear dynamical systems.
Inverse Reinforcement Learning via Deep Gaussian Process
We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations.
Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation
We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors.
